Systems and methods to control camera operations

ABSTRACT

Systems and methods to control operations of a camera based on one or more sensors attached to one or more actors. Sensor data collected from the sensors is analyzed to identify a state of an actor. The state of the actor is used to determine an operation parameter of the camera, such as the zoom level of the camera and/or the direction of the camera, and control the operation of the camera. For example, an actor who is in a state about to perform an interesting action can be selected from a plurality of actors; and the direction and the zoom level of the camera can be adjusted to focus on the selected actor in capture one or more subsequent images.

RELATED APPLICATIONS

The present application claims the benefit of the filing date of Prov.U.S. Pat. App. Ser. No. 62/117,398, filed Feb. 17, 2015 and entitled“Predictive Camera Targeting,” the entire disclosure of whichapplication is hereby incorporated herein by reference.

FIELD OF THE TECHNOLOGY

At least some embodiments disclosed herein relate to the control ofcameras in general and more specifically but not limited to autonomouscontrol of camera operations during the capture of still and/or videoimages.

BACKGROUND

Sensor data can be used to determine the motion characteristics ofsporting actions, performances of athletes, and/or states of theparticipants of sporting activities.

U.S. Pat. App. Pub. No. 2013/0346013, entitled “Method and Apparatus forDetermining Sportsman Jumps using Fuzzy Logic,” discloses a techniquethat uses fuzzy logic in the analysis of accelerometer data, generatedin response to the motions of a sportsperson, to identify a subset ofthe data as representing a jump and thus separate the jump from othermotions of the sportsperson.

U.S. Pat. No. 8,929,709, entitled “Automatic Digital Curation andTagging of Action Videos,” discloses a system for automatic digitalcuration, annotation, and tagging of action videos, where sensor datafrom a device carried by a sportsperson during a sporting activity isused to identify a sportsperson event which is then stored in aperformance database to automatically select, annotate, tag or editcorresponding video data of the sporting activity.

U.S. Pat. No. 9,060,682, entitled “Distributed Systems and Methods toMeasure and Process Sport Motions,” discloses a distributed,multi-stage, intelligent system configured to determine actionperformance characteristics parameters in action sports.

U.S. Pat. App. Pub. No. 2014/0257743, entitled “Systems and Methods forIdentifying and Characterizing Athletic Maneuvers,” discloses techniquesto automatically identify athletic maneuvers by determining, from sensordata, motion characteristics and then based on the motioncharacteristics, an athletic maneuver.

U.S. Pat. App. Pub. No. 2014/0257744, entitled “Systems and Methods forSynchronized Display of Athletic Maneuvers,” discloses techniques tosynchronize the video streams of different sportspersons based onsynchronizing the occurrences of motion characteristics identified fromsensor data such that the athletic maneuvers of the sportspersons can bevisually compared side by side.

U.S. Pat. App. Pub. No. 2015/0335949, entitled “Use of Gyro Sensors forIdentifying Athletic Maneuvers,” discloses techniques to use at leastone gyroscopic sensor in identify athletic maneuvers performed bysportspersons.

U.S. Pat. App. Pub. No. 2015/0340066, entitled “Systems and Methods forCreating and Enhancing Videos,” discloses the use of sensor data toidentify motion characteristics of sports video in combining videoselected from multiple video sources to provide a unique and richviewing experience.

The entire disclosures of the above discussed patent documents arehereby incorporated herein by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 shows a system configured to control the operations of a cameraaccording to one embodiment.

FIG. 2 shows a method to control the operations of a camera according toone embodiment.

FIG. 3 shows a method to capture video images according to oneembodiment.

FIG. 4 shows a computing apparatus to control the operations of a cameraaccording to one embodiment.

DETAILED DESCRIPTION

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding. However, in certain instances, wellknown or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure are not necessarily references to the sameembodiment; and, such references mean at least one.

Sport events in general, and action sport events in particular, are verypopular with their fans and viewing audiences. Such sport events as NFLgames, X-Games, Dew Tour, World Surfing Tour, Formula One, etc., attractthousands of fans and real time spectators.

Many of these fans have the capability, skills, and desire to film theseevents or parts of the events using their smart phones or point-of-view(POV) cameras. This richness of possible video sources is furtherincreased with the introduction of video drones and intelligent cameras.

Very often a single camera is used to record activity of multipleathletes. For example, multiple surfers or kite surfers could surf atthe same location, or several snowboarders can follow the same coursewithin a short time interval.

Under such scenarios, a camera operator has a choice to use a generalwide angle shot that covers multiple players for the entire duration ofthe event, or use a zoom view for individual players which excludesother players from the view.

A zoom view is preferable when an athlete is performing a specialactivity, such as catching a wave during surfing, making a jump, etc.However, to use the zoom view effectively, the operator needs to knowwhen each athlete is going to perform a special event, jump, catchingwave, etc. While a human operator of a camera can usually predict suchevents, such prediction during autonomous operation is often verydifficult.

At least some embodiments disclosed herein address these and otherissues with solutions that allow autonomous camera tracking of an event,along with the automatic selection of an optimal zoom level and event.

In exemplary embodiments disclosed herein, each athlete has one or moredata collection device that is mounted on the body, clothing, and/orequipment of the athlete to measure data related to the activity of theathlete. Such measurements can be performed using any number and type ofdata collection devices, including a GPS device, an inertial sensor, amagnetic sensor, a pressure sensor, and/or other devices.

FIG. 1 shows a system configured to control the operations of a cameraaccording to one embodiment.

In FIG. 1, one or more data collection devices containing the sensors(11) are coupled to a processing unit that is configured to processsensory information from the data collection devices and transmit, via acommunication link, processed sensory information to a camera controller(13) to control a respective camera (15).

The processing unit can be integrated with the sensors (11) within thesame device housing of the sensors (11), or a separate device that is incommunication with one or more sensor devices (e.g., via a wirelesspersonal area network, a wireless local area network, a wiredconnection). For example, the distributed, multi-stage system to processsensor data as disclosed in U.S. Pat. No. 9,060,682 can be used toprocess the sensor data generated from the sensors (11) that areattached to the body, clothing, and/or equipment of the athlete, thedisclosure of which patent is hereby incorporated herein by reference.

The processing unit can be co-located with the data collection devicescontaining the sensors (e.g., coupled to the athlete, the athlete'sclothing, and/or the athlete's equipment), or be located remotely fromthe data collection devices. The data collection devices may provide thesensor data to the processing unit via any suitable combination of wiredor wireless communication method.

While a single camera controller controlling a single camera is shownreceiving data collected from sensors coupled to two different athletesin FIG. 1, data from any number of different athletes, each with theirown respective set of data collection devices and processing units, canbe transmitted to one or more different camera controllers, where eachof the camera controllers can be configured to control any suitablenumber and type of cameras.

In FIG. 1, based on the analysis of the sensor data received from thesensors (11) configured in the data collection devices, the processingunit and/or the controller (13) can identify one or more “states” forthe athlete. Such states may include, for example, a description of aparticular athletic maneuver performed by the athlete (e.g.,“paddling”), a description of an activity performed by the athlete(e.g., “acceleration before Jump”), as well as other informationregarding the athlete.

For example, different states of an athlete engaging in a predeterminedsport have different patterns in sensor data (e.g., action performancecharacteristics parameters, motion characteristics). Thus, byidentifying the patterns in the current sensor data of a particularathlete, the controller (13) is configured to automatically identify thecurrent state of the athlete and/or predict the subsequent state of theathlete.

In various embodiments, the states of multiple actors are analyzed by acamera controller (13) to select an actor of interest (e.g., athlete,sportsperson, participant) and focus the camera(s) on the selected actorby directing the camera at the selected actor with a selected camerazoom level.

For example, the controller (13) of one embodiment is configured toadjust the direction of the camera (15). Based on a location sensorattached to the sportsperson to measure the location of the sportspersonand a location sensor attached to the camera, the controller (13) of oneembodiment is configured to compute a desired direction of the camera(15) and adjust the camera (15) to the desired direction. Alternativelyor in combination, the controller (13) uses image recognition techniquesto search, in a captured wide scene, the selected actor that has apredetermined visual characteristic and direct the camera to thelocation of the selected actor.

For example, the controller (13) of one embodiment is configured tocontrol the optical zoom level of a lens (17) of the camera (15) tocapture a scene limited to the selected actor and thus exclude othernon-selected actors. In general, the zoom level of the camera can beadjusted via a combination of an optical zoom function of the camera(15) and a digital zoom function.

When no actors are selected (e.g., for being important or of interest)at the moment, the controller (13) instructs the camera (15) to use ageneral pan wide angle view to capture a broader scene than a narrowscene that focuses on one or more selected actors.

In one embodiment, the processing unit may analyze information about acamera controller (e.g., received from the camera controller (13)itself) to identify a target actor and zoom level to utilize, and theninstruct the camera controller accordingly.

In other embodiments, the camera controller (13) receives the stateinformation for multiple actors from the processing units associatedwith each actor; and the camera controller (13) makes the decision as towhich actor should be focused on and at what zoom level.

In yet other embodiments, decisions regarding which actors to be focusedon and the appropriate zoom levels to be used are determined by athird-party server or other computing device in communication with boththe data processing unit(s) and the camera controller(s).

FIG. 2 shows a method to control the operations of a camera according toone embodiment. For example, the method of FIG. 2 can be performed inthe system illustrated in FIG. 1.

In FIG. 2, a computing apparatus is configured to: receive (31) sensordata from sensors (11) attached to an actor; identify (33) a motionstate of the actor from the sensor data; and select (35) an operationstate of a camera targeting the actor.

For example, from the current sensor data, the computing apparatuspredicts that the actor is about to perform an action of interest andthus directs the camera to zoom in on the actor and/or direct the camerato place the actor at a predetermined location in a scene captured bythe camera.

FIG. 3 shows a method to capture video images according to oneembodiment. For example, the method of FIG. 3 can be implemented in asystem illustrated in FIG. 1.

In FIG. 3, multiple processing units are used to determine the stateand/or performance data of multiple actors. Each respective actor has acorresponding processing unit that processes the sensor data collectedfrom the sensors (11) attached to the respective actor.

For example, a first processing unit is configured to: collect (201)sensor data from sensors (11) attached to a first actor; determine (203)a state/performance of the first actor; and send (205) thestate/performance of the first actor to a controller (13).

Similarly, a second processing unit is configured to: collect (207)sensor data from sensors (11) attached to a second actor; determine(209) a state/performance of the second actor; and send (211) thestate/performance of the second actor to the controller (13).

The controller (13) is configured to: receive (213) thestate/performance data of the actors; compare (215) thestate/performance data of the actors; select (217) an actor from theactors based on a result of the comparison; select (219) a cameraparameter (e.g., direction and zoom) based on the identification of theselected actor; and overlay (221) and tag image data captured by thecamera (15) with identification of the selected actor and sensor-basedinformation of the selected actor.

After the camera (15) captures video segments according to thedirections and zoom levels determined according to any embodimentdisclosed herein, the video segments captured by the cameras (15) can beenhanced by overlaying information on the segments. Such information mayinclude, for example, the name, date, time, location, and performancecharacteristics of the athlete. The tagged video segments may be storedin a database, and retrieved using any of the types of information usedin tagging the video. For example, the video tagging method disclosed inU.S. Pat. No. 8,929,709 can be used, the disclosure of which patent ishereby incorporated herein by reference.

Thus, among other things, embodiments of the present disclosure helpprovide the autonomous control of unattended cameras (such as drones orstationary cameras) that cover multiple athletes, and thereby provideoptimal video coverage with limited video resources.

In one embodiment, each respective actor of a set of actors in a scenehas one or more sensors in communication with a respective processingunit among the plurality of processing units; and the respectiveprocessing unit is configured to process sensor data from the one ormore sensors to identify a state of the respective actor.

A system of one embodiment includes: a plurality of processing unitsassociated with a plurality of actors participating in an activity; atleast one interface to communicate with the processing units, receivefrom the processing units data identifying states of the actorsdetermined from sensors attached to the actors, and communicate with acamera; at least one microprocessor; and a memory storing instructionsconfigured to instruct the at least one microprocessor to adjust, viathe at least one interface, an operation parameter of the camera, basedon the data identifying the states of the actors, such as a zoom levelof the camera, and a direction of the camera.

The instructions of one embodiment are further configured to instructthe at least one microprocessor to identify an actor of focus from theplurality of actors based on the states of the actors. For example, theactor of focus is selected based at least in part on performances of theplurality of actors measured using the sensors attached to the actors.For example, the actor of focus is selected based at least in part on aprediction, based on the states of the actors, that the actor of focusis about to perform an action of interest.

A method implemented in a computing device of one embodiment includes:receiving, by the computing device, sensor data from one or more sensorsattached to an actor; determining, by the computing device, a state ofthe actor based on the sensor data received from the one or moresensors; and controlling a camera based on the state of the actordetermined based on the sensor data.

For example, the controlling of the camera includes: determining anoperation parameter of the camera; and adjusting the camera based on theoperation parameter. For example, the operation parameter is a zoomlevel of the camera, or a direction of the camera.

In one embodiment, states of multiple actors are determined based on thesensor data from respective sensors attached to the actors. The statesof the actors are compared to select an operation parameter for thecamera, such as the camera direction and/or the camera zoom level. Forexample, the operation parameter can be selected to increase apercentage of an image of a first actor within an image captured by thecamera, and/or to reduce (or eliminate) a percentage of an image of thesecond actor within the image captured by the camera. The desiredoperation parameter can be computed based on a location of the actorselected as the focus of the camera.

In one embodiment, the one or more sensors are attached to a piece ofathletic equipment of the actor. Examples of the one or more sensorsinclude: a GPS device, an inertial sensor, a magnetic sensor, and apressure sensor.

In one embodiment, the computing device causes the camera to capture oneor more images based on the controlling of the direction and/or zoomlevel of the camera, overlays data derived from the sensor data on theone or more images, tags the one or more images with information derivedfrom the sensor data, and/or store the tagged images in a database,where the tagged images are retrievable via the tagged information.

The present disclosure includes methods performed by the computingdevice, non-transitory computer-readable media storing instructionsthat, when executed by such a computing device, cause the computingdevice to perform such methods, and computing devices configured toperform such methods.

FIG. 4 shows a computing apparatus to control the operations of a cameraaccording to one embodiment. For example, the computing apparatus ofFIG. 4 can be used to implement the method of FIG. 2 or FIG. 3.

In FIG. 4, the computing apparatus includes a plurality of processingunits (129) coupled with an input/output interface (115) via one or morecommunication connections. The processing units (129) are configured todetermine the state/performance of the actors in a scene based on thesensor data collected by the sensors attached to the respective actorsin the scene.

For example, a set of gyro sensors (119) attached to an actor areconfigured to measure the angular velocities of a gyro along a pluralityof axes. An analog to digital (A/D) converter (117) converts the analogsignals from the gyro sensors (119) into digital signals for theinput/output interface (115) of a computing device that performs thebias calibration. A global positioning system (GPS) receiver (127) isconfigured to measure the current location of the actor; and amagnetometer having magnetic sensors (123) with a corresponding A/Dconverter (121), and/or an accelerometer (not shown in FIG. 4) are usedto collect further sensor data related to the state, motion and/orperformance of the actor.

In FIG. 4, the computing apparatus is configured with the input/outputinterface (115) to receive the communications from the processing units(129) associated with the actors and select the camera control (131)based on the state, motion, maneuver, and/or performance of the actors.

The computing device further includes a bus (103) that connects theinput/output interface (115), at least one microprocessor (101), randomaccess memory (105), read only memory (ROM) (107), a data storage device(109), a display device (111), and an input device (113).

The memory devices (e.g., 105, 107, and 109) store instructions; and themicroprocessor(s) (101) is (are) configured via the instructions toperform various operations disclosed herein to determine the cameracontrol (131) and/or process the camera data.

The computing device of one embodiment is configured to store at least aportion of the processed sensor data (e.g., location, state, motion,maneuver and/or performance of the actors).

In FIG. 4, the display device (111) and the input device (113) areoptionally configured via the instructions to provide a user interfacethat allows the user to view the camera control (131) and/or videoimages from the cameras (e.g., 17 in FIG. 1). In one embodiment, theuser interface is configured to present the sensor data and/or theprocessing results of the sensor data that allows a human operator toselect an actor of focus and/or one or more operating parameters of thecamera (17).

FIG. 4 illustrates a data processing system according to one embodiment.While FIG. 4 illustrates various components of a computing apparatus, itis not intended to limit the disclosure to any particular architectureor manner of interconnecting the components. One embodiment may useother systems that have fewer or more components than those shown inFIG. 4.

In general, the memory devices (e.g., 105, 107, and 109) of thecomputing apparatus includes one or more of: ROM (Read Only Memory)(107), volatile RAM (Random Access Memory) (105), and non-volatilememory (109), such as hard drive, flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM) whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system which maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

At least some of the functions and operations described herein areperformed a microprocessor executing instructions stored in the memorydevices (e.g., 105, 107, and 109).

Alternatively, or in combination, the functions and operations asdescribed here can be implemented using special purpose circuitry, withor without software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

While one embodiment can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited torecordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., Compact DiskRead-Only Memory (CD ROMS), Digital Versatile Disks (DVDs), etc.), amongothers. The computer-readable media may store the instructions.

The instructions may also be embodied in digital and analogcommunication links for electrical, optical, acoustical or other formsof propagated signals, such as carrier waves, infrared signals, digitalsignals, etc. However, propagated signals, such as carrier waves,infrared signals, digital signals, etc. are not tangible machinereadable medium and are not configured to store instructions.

In general, a machine readable medium includes any mechanism thatprovides (i.e., stores and/or transmits) information in a formaccessible by a machine (e.g., a computer, network device, personaldigital assistant, manufacturing tool, any device with a set of one ormore processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

The description and drawings are illustrative and are not to beconstrued as limiting. The present disclosure is illustrative ofinventive features to enable a person skilled in the art to make and usethe techniques. Various features, as described herein, should be used incompliance with all current and future rules, laws and regulationsrelated to privacy, security, permission, consent, authorization, andothers. Numerous specific details are described to provide a thoroughunderstanding. However, in certain instances, well known or conventionaldetails are not described in order to avoid obscuring the description.References to one or an embodiment in the present disclosure are notnecessarily references to the same embodiment; and, such references meanat least one.

The use of headings herein is merely provided for ease of reference, andshall not be interpreted in any way to limit this disclosure or thefollowing claims.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,and are not necessarily all referring to separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by one embodiment and notby others. Similarly, various requirements are described which may berequirements for one embodiment but not other embodiments. Unlessexcluded by explicit description and/or apparent incompatibility, anycombination of various features described in this description is alsoincluded here. For example, the features described above in connectionwith “in one embodiment” or “in some embodiments” can be all optionallyincluded in one implementation, except where the dependency of certainfeatures on other features, as apparent from the description, may limitthe options of excluding selected features from the implementation, andincompatibility of certain features with other features, as apparentfrom the description, may limit the options of including selectedfeatures together in the implementation.

The entire disclosures of the patent documents discussed above arehereby incorporated herein by reference.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A system comprising: a plurality of processingunits associated with a plurality of actors participating in anactivity, wherein each respective actor of the actors having one or moresensors in communication with a respective processing unit among theplurality of processing units, the respective processing unit configuredto process sensor data from the one or more sensors to identify a stateof the respective actor; at least one interface to communicate with theprocessing units, receive from the processing units data identifyingstates of the actors determined from sensors attached to the actors, andcommunicate with a camera; at least one microprocessor; and a memorystoring instructions configured to instruct the at least onemicroprocessor to adjust, via the at least one interface, an operationparameter of the camera, based on the data identifying the states of theactors.
 2. The system of claim 1, wherein the instructions are furtherconfigured to instruct the at least one microprocessor to identify anactor of focus from the plurality of actors based on the states of theactors.
 3. The system of claim 2, wherein the operation parameter is oneof: a zoom level of the camera, and a direction of the camera.
 4. Thesystem of claim 2, wherein the actor of focus is selected based at leastin part on performances of the plurality of actors measured using thesensors attached to the actors.
 5. The system of claim 2, wherein theactor of focus is selected based at least in part on a prediction, basedon the states of the actors, that the actor of focus is about to performan action of interest.
 6. A method, comprising: receiving, by acomputing device, sensor data from one or more sensors attached to anactor; determining, by the computing device, a state of the actor basedon the sensor data received from the one or more sensors; andcontrolling a camera based on the state of the actor determined based onthe sensor data.
 7. The method of claim 6, wherein the controlling ofthe camera includes: determining an operation parameter of the camera;and adjusting the camera based on the operation parameter.
 8. The methodof claim 7, wherein the operation parameter includes a zoom level of thecamera.
 9. The method of claim 7, wherein the operation parameterincludes a direction of the camera.
 10. The method of claim 6, whereinthe actor is a first actor; and the method further comprises: receiving,by the computing device, second sensor data from one or more sensorsattached to a second actor; determining, by the computing device, astate of the second actor based on the second sensor data; comparing thestate of the first actor and the state of the second actor; andselecting an operation parameter for the camera based on the comparingof the state of the first actor and the state of the second actor. 11.The method of claim 6, wherein the selecting of the operation parameterincludes: selecting the first actor from a plurality of actors includingthe first actor and the second actor, based on comparing the state ofthe first actor and the state of the second actor; determining theoperation parameter based on an identification of the first actorselected based on the comparing.
 12. The method of claim 11, wherein theoperation parameter is selected to increase a percentage of an image ofthe first actor within an image captured by the camera.
 13. The methodof claim 12, wherein the operation parameter is selected to reduce apercentage of an image of the second actor within the image captured bythe camera.
 14. The method of claim 11, wherein the operation parameteris selected based on a location of the first actor.
 15. The method ofclaim 6, wherein the one or more sensors are attached to a piece ofathletic equipment of the actor.
 16. The method of claim 6, wherein theone or more sensors include at least one of: a GPS device, an inertialsensor, a magnetic sensor, and a pressure sensor.
 17. The method ofclaim 6, further comprising: causing the camera to capture one or moreimages based on the controlling of the camera.
 18. The method of claim17, further comprising: overlaying data derived from the sensor data onthe one or more images.
 19. The method of claim 17, further comprising:tagging the one or more images with information derived from the sensordata; and storing the tagged images in a database, wherein the taggedimages are retrievable via the tagged information.
 20. A non-transitorycomputer-readable medium storing instructions that, when executed by acomputing device, cause the computing device to perform a method, themethod comprising: receiving, by the computing device, sensor data fromone or more sensors attached to an actor; determining, by the computingdevice, a state of the actor based on the sensor data received from theone or more sensors; and controlling a camera based on the state of theactor determined based on the sensor data.
 21. A method, comprising:receiving, by a computing device, sensor data from one or more sensorsattached to an actor; processing, by the computing device, the sensordata to generate information indicative of a state of the actor;presenting, by the computing device, the information on a user interfaceto cause a human operator to predict an action of the actor, and controla camera based on the action predicted based on the informationpresented in the user interface.